The k-NN classifier and self-adaptive Hotelling data reduction technique in handwritten signatures recognition

Abstract

The paper proposes a novel signature verification concept. This new approach uses appropriate similarity coefficients to evaluate the associations between the signature features. This association, called the new composed feature, enables the calculation of a new form of similarity between objects. The most important advantage of the proposed solution is case-by-case matching of similarity coefficients to a signature features, which can be utilized to assess whether a given signature is genuine or forged. The procedure, as described, has been repeated for each person presented in a signatures database. In the verification stage, a two-class classifier recognizes genuine and forged signatures. In this paper, a broad range of classifiers are evaluated. These classifiers all operate on features observed and computed during the data preparation stage. The set of signature composed features of a given person can be reduced what decrease verification error. Such a phenomenon does not occur for the raw features. The approach proposed was tested in a practical environment, with handwritten signatures used as the objects to be compared. The high level of signature recognition obtained confirms that the proposed methodology is efficient and that it can be adapted to accommodate as yet unknown features. The approach proposed can be incorporated into biometric systems.

Dynamic signature verification method based on association of features with similarity measures

“…S Ω : genuine signature of the person Q if #D1 > #D2 forged signature of the person Q if #D1 ≤ #D2 Details of the signature verification method are widely also described in previous work of the Authors [42,43].…”

Problems of Selecting the Wavelet Transform Parameters in the Aspect of Surface Texture Analysis

“…( 12) is compared to the critical value of the distribution χ 2 at the assumed level of significance α. The covariance matrix is not homogeneous when the dependency ( 15) is satisfied [24,25].…”

“…( 17), taking into account dependence (18). Otherwise, it should be modified using dependence (19) [24,25].…”

Classifier Selection for Motor Imagery Brain Computer Interface

“…In MI-BCI and other biometric systems, simple individual classifiers are usually used in the classification process, e.g. linear discriminant analysis (LDA) [6], Fisher linear discriminant analysis (FDA) [13], k-NN classifier [17], [16], distinction sensitive learning vector quantization classifier (DSLVQ) [15] or minimum Mahalanobis distance (MDA) classifier [14]. The boosting algorithms are also an effective method of producing a very accurate classification rule [9], however, they are rarely used in BCI domain [12].…”